VTI researchers Anna Anund and Björn Peters have been working with an EU project called AWAKE. The aim of the project that had its “final review” in Italy in September 2004 is to develop a system that can reliably detect worsening driver performance, caused by drowsiness or lack of concentration.

The system which has been developed in AWAKE is based on SAVE, another EU project which was finalised in autumn 1998.

– In SAVE, we produced a prototype of a system which detects problems in a driver’s situation for example, alcohol or drug abuse, drowsiness, health problems or long periods of lack of concentration. Of which all can ultimately lead to the risk of having an accident, says Björn Peters.

As well as the AWAKE’s detection system there is also a module that assesses traffic risks and a warning system.

Studies in VTI’s simulatorsThe AWAKE system’s detection ability is based on data collected from various behaviour measurements and different driving ability measurements. It gives different kinds of warnings depending on the level of calculated risk and how alert the driver is. Adjustments to the system have been made to suit various types of vehicles such as cars and lorries.

– In the AWAKE project, VTI have been in charge of planning the evaluation studies and carrying out two experimental investigations in VTI’s simulators, says Anna Anund.

Both R&D organisations and companies from the vehicle and electronics industries have been included in the project consortium

Hypovigilance Diagnosis ModuleThe Hypovigilance Diagnosis Module (HDM) diagnoses the driver’s wakefulness in real time. The diagnosis is made using an intelligent algorithm which works with data from different sensors (eye movements and grip on the steering wheel), and with data on how the driver is driving, for example, from side to side. The aim is to produce a system that is capable of diagnosing a tired driver with 90% accuracy with only 1% chance of a false alarm. It is set for driving on main roads. In order to attain this accuracy goal, the HDM calculations are based on the individual driver’s usual way of driving, which means when alert. A “smart card” is used to provide the system with information about who is driving. If the driver is unknown to the system the system collects new data about the person when she or he starts driving. The system learns how the driver drives and uses this as a starting point for any evaluation.

HDM is made up of various subsystems, which deliver different independent diagnoses. The final diagnosis is made on three levels:

•The driver is awake

•The driver may be tired

•The driver is tired.

Traffic Risk Estimation ModuleIn order to judge whether an ongoing traffic situation is risky or not, there is a module which uses data from a digital map, a positioning system and an anti-collision radar. The output data from the module is used as input data for the HDM and for a warning system where ongoing traffic situations are evaluated. This takes place in a Traffic Risk Emulation Module (TRE). The traffic risk is evaluated on two levels: a general level and a specific level.

Hierarchical ManagerHierarchical Manager (HM) is the module that co-ordinates HDM and TRE and hosts the warning system. HM uses data from both HDM and TRE and evaluates what should be a suitable warning strategy in a given situation.

Driver Warning SystemThe “Driving Warning System” (DWS) uses noise, light and movement in different combinations as warnings. The noise alert uses different kinds of warning sounds to get the driver’s attention and verbal messages to tell the driver why she or he is being given a warning. The visual warnings are located in the inside rear view mirror. In the rear view mirror, there is a unit with a smart card installed and buttons to start or stop the system. When the system decides the driver has to be warned using movement there is a vibrator alert located in the safety belt.

AWAKE = System for Effective Assessment of Driver Vigilance and Warning According to Traffic Risk Estimation

SAVE = System for Effective Assessment of Driver State and Vehicle Control in Emergency Situations

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